Combining data science methods with flow simulation in shale resources development

There are two approaches to uncertainty quantification in petroleum reservoirs in general: data-driven modelling and reservoir simulation. In shale reservoirs data-driven modelling has become favorable, mainly due to two reasons. Firstly, the high number of wells drilled leads to large data volumes and necessity for rapid decision making. Secondly, physical processes of hydraulic fracturing and shale production, its connection to natural fractures are poorly understood. In this research, I would like to determine compatibility of data-driven modelling and direct reservoir simulation results in shale reservoirs. Do we get the same answers for well placement and identifying most important for production factors using two different methods? Does forecast quality suffer without reservoir simulation? And as the result I would like to create synthesis of two methods, combining ability of working with large datasets of data-driven modelling and better physics from reservoir simulation. We have started by applying data-driven approach to Niobrara, Eagle Ford and Duvernay plays, the next step is reservoir simulation for particular wells. Alexander Bakay
Paper: Integrating Geostatistical Modeling with Machine Learning for Production Forecast in Shale Reservoirs: Case Study from Eagle Ford